AIMC Topic: Machine Learning

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AI-based forecasting of ethanol fermentation using yeast morphological data.

Bioscience, biotechnology, and biochemistry
Several industries require getting information of products as soon as possible during fermentation. However, the trade-off between sensing speed and data quantity presents challenges for forecasting fermentation product yields. In this study, we trie...

Detection of subtype-specific breast cancer surface protein biomarkers via a novel transcriptomics approach.

Bioscience reports
BACKGROUND: Cell-surface proteins have been widely used as diagnostic and prognostic markers in cancer research and as targets for the development of anticancer agents. So far, very few attempts have been made to characterize the surfaceome of patien...

Co-evolutionary distance predictions contain flexibility information.

Bioinformatics (Oxford, England)
MOTIVATION: Co-evolution analysis can be used to accurately predict residue-residue contacts from multiple sequence alignments. The introduction of machine-learning techniques has enabled substantial improvements in precision and a shift from predict...

Graph representation learning for structural proteomics.

Emerging topics in life sciences
The field of structural proteomics, which is focused on studying the structure-function relationship of proteins and protein complexes, is experiencing rapid growth. Since the early 2000s, structural databases such as the Protein Data Bank are storin...

[Predicting prolonged length of intensive care unit stay machine learning].

Beijing da xue xue bao. Yi xue ban = Journal of Peking University. Health sciences
OBJECTIVE: To construct length of intensive care unit (ICU) stay (LOS-ICU) prediction models for ICU patients, based on three machine learning models support vector machine (SVM), classification and regression tree (CART), and random forest (RF), and...

Decoding the effects of synonymous variants.

Nucleic acids research
Synonymous single nucleotide variants (sSNVs) are common in the human genome but are often overlooked. However, sSNVs can have significant biological impact and may lead to disease. Existing computational methods for evaluating the effect of sSNVs su...

Predictive Coding, Variational Autoencoders, and Biological Connections.

Neural computation
We present a review of predictive coding, from theoretical neuroscience, and variational autoencoders, from machine learning, identifying the common origin and mathematical framework underlying both areas. As each area is prominent within its respect...

Neural Networks With Disabilities: An Introduction to Complementary Artificial Intelligence.

Neural computation
Machine learning is a good tool to simulate human cognitive skills as it is about mapping perceived information to various labels or action choices, aiming at optimal behavior policies for a human or an artificial agent operating in the environment. ...

iDNA-ABT: advanced deep learning model for detecting DNA methylation with adaptive features and transductive information maximization.

Bioinformatics (Oxford, England)
MOTIVATION: DNA methylation plays an important role in epigenetic modification, the occurrence, and the development of diseases. Therefore, identification of DNA methylation sites is critical for better understanding and revealing their functional me...

DTI-Voodoo: machine learning over interaction networks and ontology-based background knowledge predicts drug-target interactions.

Bioinformatics (Oxford, England)
MOTIVATION: In silico drug-target interaction (DTI) prediction is important for drug discovery and drug repurposing. Approaches to predict DTIs can proceed indirectly, top-down, using phenotypic effects of drugs to identify potential drug targets, or...